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tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: roberta-base-mr-6000ar
results: []
---
# roberta-base-mr-6000ar
This model was trained from scratch on the Internal Selection for BDC Satria Data 2024 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0515
- Accuracy: 0.9413
- Precision: 0.9643
- Recall: 0.9265
- F1: 0.9450
## Model description
Training dataset was augmented with the paraphrasing method to generate 6000 extra data.
## Intended uses & limitations
This model was not the model used for the final submission on the internal selection.
## Training and evaluation data
The training dataset had 1500 rows of data, and an extra 6000 augmented data. The evaluation dataset had 500 rows of data.
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.0185 | 1.0 | 821 | 0.0800 | 0.9173 | 0.8879 | 0.9706 | 0.9274 |
| 0.0121 | 2.0 | 1642 | 0.0789 | 0.9147 | 0.9778 | 0.8627 | 0.9167 |
| 0.0101 | 3.0 | 2463 | 0.0515 | 0.9413 | 0.9643 | 0.9265 | 0.9450 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
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